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lily-goli/
recuriosity

Name: recuriosity
Availability: InStock

reinforcement-learning

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Remember to be Curious — Pretrained Checkpoints

Pretrained checkpoints for:

Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration
Lily Goli, Justin Kerr, Daniele Reda, Alec Jacobson, Andrea Tagliasacchi, Angjoo Kanazawa
University of Toronto · UC Berkeley · Wayve · Vector Institute · Simon Fraser University
[Project page] · [Code]

Model description

A reinforcement learning agent that learns to explore 3D indoor scenes using only an RGB camera. The policy uses a DINO ViT-B/8 visual encoder with a sliding-window transformer backbone (KV-cached), trained with PPO. Intrinsic reward is derived from real-time Gaussian Splatting (GSplat) reconstruction quality.

Checkpoints

File	Description
`explorer.pt`	Main exploration policy, trained on HM3D
`apple_finetuned.pt`	Apple-picking fine-tune (from `explorer.pt`)
`image_goal_finetuned.pt`	Image-goal navigation fine-tune (from `explorer.pt`)

Usage

See the code repository for full installation and evaluation instructions.

Training data

Trained on Habitat-Matterport 3D (HM3D) scenes using the Habitat-Sim simulator.

lily-goli/recuriosity